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Posted to github@beam.apache.org by GitBox <gi...@apache.org> on 2020/05/18 20:59:34 UTC
[GitHub] [beam] aaltay commented on a change in pull request #11075: [BEAM-9421] Website section that describes getting predictions using AI Platform Prediciton
aaltay commented on a change in pull request #11075:
URL: https://github.com/apache/beam/pull/11075#discussion_r426889254
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File path: website/www/site/content/en/documentation/patterns/ai-platform.md
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@@ -0,0 +1,79 @@
+---
+title: "AI Platform integration patterns"
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# AI Platform integration patterns
+
+This page describes common patterns in pipelines with Google Cloud AI Platform transforms.
+
+{{< language-switcher java py >}}
+
+## Getting predictions
+
+This section shows how to use [Google Cloud AI Platform Prediction](https://cloud.google.com/ai-platform/prediction/docs/overview) to make predictions about new data from a cloud-hosted machine learning model.
+
+[tfx_bsl](https://github.com/tensorflow/tfx-bsl) is a library with a Beam PTransform called `RunInference`. `RunInference` is able to perform an inference that can use an external service endpoint for receiving data. When using a service endpoint, the transform takes a PCollection of type `tf.train.Example` and, for every batch of elements, sends a request to AI Platform Prediction. The size of a batch may vary. For more details on how Beam finds the best batch size, refer to a docstring for [BatchElements](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.util.html?highlight=batchelements#apache_beam.transforms.util.BatchElements).
Review comment:
`tf.train.Example` -> `tf.train.Example` or `tf.train.SequenceExample`
##########
File path: website/www/site/content/en/documentation/patterns/ai-platform.md
##########
@@ -0,0 +1,79 @@
+---
+title: "AI Platform integration patterns"
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# AI Platform integration patterns
+
+This page describes common patterns in pipelines with Google Cloud AI Platform transforms.
+
+{{< language-switcher java py >}}
+
+## Getting predictions
+
+This section shows how to use [Google Cloud AI Platform Prediction](https://cloud.google.com/ai-platform/prediction/docs/overview) to make predictions about new data from a cloud-hosted machine learning model.
+
+[tfx_bsl](https://github.com/tensorflow/tfx-bsl) is a library with a Beam PTransform called `RunInference`. `RunInference` is able to perform an inference that can use an external service endpoint for receiving data. When using a service endpoint, the transform takes a PCollection of type `tf.train.Example` and, for every batch of elements, sends a request to AI Platform Prediction. The size of a batch may vary. For more details on how Beam finds the best batch size, refer to a docstring for [BatchElements](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.util.html?highlight=batchelements#apache_beam.transforms.util.BatchElements).
+
+ The transform produces a PCollection of type `PredictionLog`, which contains predictions.
+
+Before getting started, deploy a TensorFlow model to AI Platform Prediction. The cloud service manages the infrastructure needed to handle prediction requests in both efficient and scalable way. Do note that only TensorFlow models are supported by the transform. For more information, see [Exporting a SavedModel for prediction](https://cloud.google.com/ai-platform/prediction/docs/exporting-savedmodel-for-prediction).
+
+Once a machine learning model is deployed, prepare a list of instances to get predictions for. To send binary data, make sure that the name of an input ends in `_bytes`. This will base64-encode data before sending a request.
Review comment:
Is this still applicable?
/cc @rose-rong-liu
##########
File path: website/www/site/content/en/documentation/patterns/ai-platform.md
##########
@@ -0,0 +1,79 @@
+---
+title: "AI Platform integration patterns"
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# AI Platform integration patterns
+
+This page describes common patterns in pipelines with Google Cloud AI Platform transforms.
+
+{{< language-switcher java py >}}
+
+## Getting predictions
+
+This section shows how to use [Google Cloud AI Platform Prediction](https://cloud.google.com/ai-platform/prediction/docs/overview) to make predictions about new data from a cloud-hosted machine learning model.
+
+[tfx_bsl](https://github.com/tensorflow/tfx-bsl) is a library with a Beam PTransform called `RunInference`. `RunInference` is able to perform an inference that can use an external service endpoint for receiving data. When using a service endpoint, the transform takes a PCollection of type `tf.train.Example` and, for every batch of elements, sends a request to AI Platform Prediction. The size of a batch may vary. For more details on how Beam finds the best batch size, refer to a docstring for [BatchElements](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.util.html?highlight=batchelements#apache_beam.transforms.util.BatchElements).
Review comment:
The size of a batch may vary. -> The size of a batch is automatically computed.
##########
File path: website/www/site/content/en/documentation/patterns/ai-platform.md
##########
@@ -0,0 +1,79 @@
+---
+title: "AI Platform integration patterns"
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# AI Platform integration patterns
+
+This page describes common patterns in pipelines with Google Cloud AI Platform transforms.
+
+{{< language-switcher java py >}}
+
+## Getting predictions
+
+This section shows how to use [Google Cloud AI Platform Prediction](https://cloud.google.com/ai-platform/prediction/docs/overview) to make predictions about new data from a cloud-hosted machine learning model.
+
+[tfx_bsl](https://github.com/tensorflow/tfx-bsl) is a library with a Beam PTransform called `RunInference`. `RunInference` is able to perform an inference that can use an external service endpoint for receiving data. When using a service endpoint, the transform takes a PCollection of type `tf.train.Example` and, for every batch of elements, sends a request to AI Platform Prediction. The size of a batch may vary. For more details on how Beam finds the best batch size, refer to a docstring for [BatchElements](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.util.html?highlight=batchelements#apache_beam.transforms.util.BatchElements).
+
+ The transform produces a PCollection of type `PredictionLog`, which contains predictions.
+
+Before getting started, deploy a TensorFlow model to AI Platform Prediction. The cloud service manages the infrastructure needed to handle prediction requests in both efficient and scalable way. Do note that only TensorFlow models are supported by the transform. For more information, see [Exporting a SavedModel for prediction](https://cloud.google.com/ai-platform/prediction/docs/exporting-savedmodel-for-prediction).
+
+Once a machine learning model is deployed, prepare a list of instances to get predictions for. To send binary data, make sure that the name of an input ends in `_bytes`. This will base64-encode data before sending a request.
+
+### Example
+Here is an example of a pipeline that reads input instances from the file, converts JSON objects to `tf.train.Example` objects and sends data to AI Platform Prediction. The content of a file can look like this:
+
+```
+{"input": "the quick brown"}
+{"input": "la bruja le"}
+```
+
+The example creates `tf.train.BytesList` instances, thus it expects byte-like strings as input. However, other data types, like `tf.train.FloatList` and `tf.train.Int64List`, are also supported by the transform.
+
+Here is the code:
+
+{{< highlight java >}}
+// Getting predictions is not yet available for Java. [BEAM-9501]
+{{< /highlight >}}
+
+{{< highlight py >}}
+import json
+
+import apache_beam as beam
+
+import tensorflow as tf
+from tfx_bsl.beam.run_inference import RunInference
+from tfx_bsl.proto import model_spec_pb2
+
+def convert_json_to_tf_example(json_obj):
+ dict_ = json.loads(json_obj)
Review comment:
remove the underscore. dict_ -> dict
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